{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<p>3272043 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id  item_id cate_id action_type   timestamp\n",
       "action_time                                                   \n",
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       "1487174402    7500423   531150     1_9   deep_view  1487174402\n",
       "1487174403   11227572   522855    1_17   deep_view  1487174403\n",
       "1487174403   12431632   534450     1_1   deep_view  1487174403\n",
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       "1487174403   14380477   535477     1_1   deep_view  1487174403\n",
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       "1487174403    6088961   533833     1_1   deep_view  1487174403\n",
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       "1487174404   11996914   530796    1_14        view  1487174404\n",
       "1487174404   14378544   515907     1_6        view  1487174404\n",
       "1487174404   16401821   535084    1_23   deep_view  1487174404\n",
       "1487174404    7908369   503571    1_23        view  1487174404\n",
       "1487174404    8462794   500412    1_12   deep_view  1487174404\n",
       "...               ...      ...     ...         ...         ...\n",
       "1487433595   14503435   556944    1_11        view  1487433595\n",
       "1487433595   14785354   557391    1_13        view  1487433595\n",
       "1487433595    6046417   549425     1_3        view  1487433595\n",
       "1487433595    7994897   542900     1_6   deep_view  1487433595\n",
       "1487433595    8046141   548638     1_3        view  1487433595\n",
       "1487433596   13642170   557656    1_12   deep_view  1487433596\n",
       "1487433596   14597382   529781     3_2        view  1487433596\n",
       "1487433596   15123124   542966     1_6   deep_view  1487433596\n",
       "1487433596    1552565   540172     1_6   deep_view  1487433596\n",
       "1487433596   16801260   557673     1_6   deep_view  1487433596\n",
       "1487433596    9526597   556855     1_1   deep_view  1487433596\n",
       "1487433597   13050562   522664     3_2        view  1487433597\n",
       "1487433597   13375001   542997     1_3   deep_view  1487433597\n",
       "1487433597   14597382   524113     3_2        view  1487433597\n",
       "1487433597   15488166   555820     1_6   deep_view  1487433597\n",
       "1487433597   16243453   557432    1_23        view  1487433597\n",
       "1487433597   16487497   545043     1_6   deep_view  1487433597\n",
       "1487433597   17026753   543007     1_1   deep_view  1487433597\n",
       "1487433598   11283320   553331     1_6   deep_view  1487433598\n",
       "1487433598   12982345   514928     1_6   deep_view  1487433598\n",
       "1487433598    1469607   555989     1_6        view  1487433598\n",
       "1487433598    3063774   557764     1_2   deep_view  1487433598\n",
       "1487433598    9121520   552734     1_1   deep_view  1487433598\n",
       "1487433599   10383492   536548     1_1        view  1487433599\n",
       "1487433599   13006837   552829     1_9   deep_view  1487433599\n",
       "1487433599   13992209   548120    1_11        view  1487433599\n",
       "1487433599   14597382   453373     3_2        view  1487433599\n",
       "1487433599   16699556   523378     3_6        view  1487433599\n",
       "1487433599    4496330   557706     1_6        view  1487433599\n",
       "1487433599    9391042   555820     1_6        view  1487433599\n",
       "\n",
       "[3272043 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = pd.read_csv('../raw/train.csv', index_col= 'action_time', parse_dates=True)\n",
    "raw['timestamp'] = raw.index\n",
    "raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      "text/plain": [
       "              user_id item_id cate_id action_type   timestamp\n",
       "action_time                                                  \n",
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       "2017-02-16   13794253  510089    1_27   deep_view  1487174400"
      ]
     },
     "execution_count": 3,
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   "source": [
    "ts = pd.to_datetime((raw.index + 60*60*8) * 10 ** 9) \n",
    "rawd = pd.DataFrame(data= raw.values, index=ts, columns=raw.columns)\n",
    "rawd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th>2017-02-16 00:00:03</th>\n",
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       "      <th>2017-02-16 00:00:03</th>\n",
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       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174403</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16 00:00:04</th>\n",
       "      <td>11996914</td>\n",
       "      <td>530796</td>\n",
       "      <td>1_14</td>\n",
       "      <td>view</td>\n",
       "      <td>1487174404</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16 00:00:04</th>\n",
       "      <td>14378544</td>\n",
       "      <td>515907</td>\n",
       "      <td>1_6</td>\n",
       "      <td>view</td>\n",
       "      <td>1487174404</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16 00:00:04</th>\n",
       "      <td>16401821</td>\n",
       "      <td>535084</td>\n",
       "      <td>1_23</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174404</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16 00:00:04</th>\n",
       "      <td>7908369</td>\n",
       "      <td>503571</td>\n",
       "      <td>1_23</td>\n",
       "      <td>view</td>\n",
       "      <td>1487174404</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16 00:00:04</th>\n",
       "      <td>8462794</td>\n",
       "      <td>500412</td>\n",
       "      <td>1_12</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174404</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:55</th>\n",
       "      <td>14503435</td>\n",
       "      <td>556944</td>\n",
       "      <td>1_11</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:55</th>\n",
       "      <td>14785354</td>\n",
       "      <td>557391</td>\n",
       "      <td>1_13</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:55</th>\n",
       "      <td>6046417</td>\n",
       "      <td>549425</td>\n",
       "      <td>1_3</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:55</th>\n",
       "      <td>7994897</td>\n",
       "      <td>542900</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433595</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:55</th>\n",
       "      <td>8046141</td>\n",
       "      <td>548638</td>\n",
       "      <td>1_3</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>13642170</td>\n",
       "      <td>557656</td>\n",
       "      <td>1_12</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>14597382</td>\n",
       "      <td>529781</td>\n",
       "      <td>3_2</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>15123124</td>\n",
       "      <td>542966</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>1552565</td>\n",
       "      <td>540172</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>16801260</td>\n",
       "      <td>557673</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:56</th>\n",
       "      <td>9526597</td>\n",
       "      <td>556855</td>\n",
       "      <td>1_1</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433596</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>13050562</td>\n",
       "      <td>522664</td>\n",
       "      <td>3_2</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>13375001</td>\n",
       "      <td>542997</td>\n",
       "      <td>1_3</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>14597382</td>\n",
       "      <td>524113</td>\n",
       "      <td>3_2</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>15488166</td>\n",
       "      <td>555820</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>16243453</td>\n",
       "      <td>557432</td>\n",
       "      <td>1_23</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>16487497</td>\n",
       "      <td>545043</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:57</th>\n",
       "      <td>17026753</td>\n",
       "      <td>543007</td>\n",
       "      <td>1_1</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433597</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:58</th>\n",
       "      <td>11283320</td>\n",
       "      <td>553331</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433598</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:58</th>\n",
       "      <td>12982345</td>\n",
       "      <td>514928</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433598</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:58</th>\n",
       "      <td>1469607</td>\n",
       "      <td>555989</td>\n",
       "      <td>1_6</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433598</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:58</th>\n",
       "      <td>3063774</td>\n",
       "      <td>557764</td>\n",
       "      <td>1_2</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433598</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:58</th>\n",
       "      <td>9121520</td>\n",
       "      <td>552734</td>\n",
       "      <td>1_1</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433598</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>10383492</td>\n",
       "      <td>536548</td>\n",
       "      <td>1_1</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>13006837</td>\n",
       "      <td>552829</td>\n",
       "      <td>1_9</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>13992209</td>\n",
       "      <td>548120</td>\n",
       "      <td>1_11</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>14597382</td>\n",
       "      <td>453373</td>\n",
       "      <td>3_2</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>16699556</td>\n",
       "      <td>523378</td>\n",
       "      <td>3_6</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>4496330</td>\n",
       "      <td>557706</td>\n",
       "      <td>1_6</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-18 23:59:59</th>\n",
       "      <td>9391042</td>\n",
       "      <td>555820</td>\n",
       "      <td>1_6</td>\n",
       "      <td>view</td>\n",
       "      <td>1487433599</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3272043 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      user_id item_id cate_id action_type   timestamp  view  \\\n",
       "action_time                                                                   \n",
       "2017-02-16 00:00:00  11482147  492681    1_11        view  1487174400     1   \n",
       "2017-02-16 00:00:00  12070750  457406    1_14   deep_view  1487174400     0   \n",
       "2017-02-16 00:00:00  12431632  527476     1_1        view  1487174400     1   \n",
       "2017-02-16 00:00:00  13397746  531771     1_6   deep_view  1487174400     0   \n",
       "2017-02-16 00:00:00  13794253  510089    1_27   deep_view  1487174400     0   \n",
       "2017-02-16 00:00:00  14378544  535335     1_6   deep_view  1487174400     0   \n",
       "2017-02-16 00:00:00   1705634  535202    1_10        view  1487174400     1   \n",
       "2017-02-16 00:00:00   6943823  478183     1_3   deep_view  1487174400     0   \n",
       "2017-02-16 00:00:01   5902475  524378     1_6        view  1487174401     1   \n",
       "2017-02-16 00:00:02  12646404  529724     1_3        view  1487174402     1   \n",
       "2017-02-16 00:00:02  13156768  426867    1_17        view  1487174402     1   \n",
       "2017-02-16 00:00:02  14367772  484321     1_3        view  1487174402     1   \n",
       "2017-02-16 00:00:02  15967218  468533     1_3   deep_view  1487174402     0   \n",
       "2017-02-16 00:00:02   7500423  531150     1_9   deep_view  1487174402     0   \n",
       "2017-02-16 00:00:03  11227572  522855    1_17   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:03  12431632  534450     1_1   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:03  12799141  531289     1_1        view  1487174403     1   \n",
       "2017-02-16 00:00:03  14380477  535477     1_1   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:03  15894951  491934    1_23        view  1487174403     1   \n",
       "2017-02-16 00:00:03  15905528  432274     1_2   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:03   1904514  530486    1_13        view  1487174403     1   \n",
       "2017-02-16 00:00:03   2415271  535363     1_6        view  1487174403     1   \n",
       "2017-02-16 00:00:03   2472319  535334    1_23        view  1487174403     1   \n",
       "2017-02-16 00:00:03   6088961  533833     1_1   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:03   8350505  527149     1_6   deep_view  1487174403     0   \n",
       "2017-02-16 00:00:04  11996914  530796    1_14        view  1487174404     1   \n",
       "2017-02-16 00:00:04  14378544  515907     1_6        view  1487174404     1   \n",
       "2017-02-16 00:00:04  16401821  535084    1_23   deep_view  1487174404     0   \n",
       "2017-02-16 00:00:04   7908369  503571    1_23        view  1487174404     1   \n",
       "2017-02-16 00:00:04   8462794  500412    1_12   deep_view  1487174404     0   \n",
       "...                       ...     ...     ...         ...         ...   ...   \n",
       "2017-02-18 23:59:55  14503435  556944    1_11        view  1487433595     1   \n",
       "2017-02-18 23:59:55  14785354  557391    1_13        view  1487433595     1   \n",
       "2017-02-18 23:59:55   6046417  549425     1_3        view  1487433595     1   \n",
       "2017-02-18 23:59:55   7994897  542900     1_6   deep_view  1487433595     0   \n",
       "2017-02-18 23:59:55   8046141  548638     1_3        view  1487433595     1   \n",
       "2017-02-18 23:59:56  13642170  557656    1_12   deep_view  1487433596     0   \n",
       "2017-02-18 23:59:56  14597382  529781     3_2        view  1487433596     1   \n",
       "2017-02-18 23:59:56  15123124  542966     1_6   deep_view  1487433596     0   \n",
       "2017-02-18 23:59:56   1552565  540172     1_6   deep_view  1487433596     0   \n",
       "2017-02-18 23:59:56  16801260  557673     1_6   deep_view  1487433596     0   \n",
       "2017-02-18 23:59:56   9526597  556855     1_1   deep_view  1487433596     0   \n",
       "2017-02-18 23:59:57  13050562  522664     3_2        view  1487433597     1   \n",
       "2017-02-18 23:59:57  13375001  542997     1_3   deep_view  1487433597     0   \n",
       "2017-02-18 23:59:57  14597382  524113     3_2        view  1487433597     1   \n",
       "2017-02-18 23:59:57  15488166  555820     1_6   deep_view  1487433597     0   \n",
       "2017-02-18 23:59:57  16243453  557432    1_23        view  1487433597     1   \n",
       "2017-02-18 23:59:57  16487497  545043     1_6   deep_view  1487433597     0   \n",
       "2017-02-18 23:59:57  17026753  543007     1_1   deep_view  1487433597     0   \n",
       "2017-02-18 23:59:58  11283320  553331     1_6   deep_view  1487433598     0   \n",
       "2017-02-18 23:59:58  12982345  514928     1_6   deep_view  1487433598     0   \n",
       "2017-02-18 23:59:58   1469607  555989     1_6        view  1487433598     1   \n",
       "2017-02-18 23:59:58   3063774  557764     1_2   deep_view  1487433598     0   \n",
       "2017-02-18 23:59:58   9121520  552734     1_1   deep_view  1487433598     0   \n",
       "2017-02-18 23:59:59  10383492  536548     1_1        view  1487433599     1   \n",
       "2017-02-18 23:59:59  13006837  552829     1_9   deep_view  1487433599     0   \n",
       "2017-02-18 23:59:59  13992209  548120    1_11        view  1487433599     1   \n",
       "2017-02-18 23:59:59  14597382  453373     3_2        view  1487433599     1   \n",
       "2017-02-18 23:59:59  16699556  523378     3_6        view  1487433599     1   \n",
       "2017-02-18 23:59:59   4496330  557706     1_6        view  1487433599     1   \n",
       "2017-02-18 23:59:59   9391042  555820     1_6        view  1487433599     1   \n",
       "\n",
       "                     deep_view  collect  share  comment  \n",
       "action_time                                              \n",
       "2017-02-16 00:00:00          0        0      0        0  \n",
       "2017-02-16 00:00:00          1        0      0        0  \n",
       "2017-02-16 00:00:00          0        0      0        0  \n",
       "2017-02-16 00:00:00          1        0      0        0  \n",
       "2017-02-16 00:00:00          1        0      0        0  \n",
       "2017-02-16 00:00:00          1        0      0        0  \n",
       "2017-02-16 00:00:00          0        0      0        0  \n",
       "2017-02-16 00:00:00          1        0      0        0  \n",
       "2017-02-16 00:00:01          0        0      0        0  \n",
       "2017-02-16 00:00:02          0        0      0        0  \n",
       "2017-02-16 00:00:02          0        0      0        0  \n",
       "2017-02-16 00:00:02          0        0      0        0  \n",
       "2017-02-16 00:00:02          1        0      0        0  \n",
       "2017-02-16 00:00:02          1        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:03          0        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:03          0        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:03          0        0      0        0  \n",
       "2017-02-16 00:00:03          0        0      0        0  \n",
       "2017-02-16 00:00:03          0        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:03          1        0      0        0  \n",
       "2017-02-16 00:00:04          0        0      0        0  \n",
       "2017-02-16 00:00:04          0        0      0        0  \n",
       "2017-02-16 00:00:04          1        0      0        0  \n",
       "2017-02-16 00:00:04          0        0      0        0  \n",
       "2017-02-16 00:00:04          1        0      0        0  \n",
       "...                        ...      ...    ...      ...  \n",
       "2017-02-18 23:59:55          0        0      0        0  \n",
       "2017-02-18 23:59:55          0        0      0        0  \n",
       "2017-02-18 23:59:55          0        0      0        0  \n",
       "2017-02-18 23:59:55          1        0      0        0  \n",
       "2017-02-18 23:59:55          0        0      0        0  \n",
       "2017-02-18 23:59:56          1        0      0        0  \n",
       "2017-02-18 23:59:56          0        0      0        0  \n",
       "2017-02-18 23:59:56          1        0      0        0  \n",
       "2017-02-18 23:59:56          1        0      0        0  \n",
       "2017-02-18 23:59:56          1        0      0        0  \n",
       "2017-02-18 23:59:56          1        0      0        0  \n",
       "2017-02-18 23:59:57          0        0      0        0  \n",
       "2017-02-18 23:59:57          1        0      0        0  \n",
       "2017-02-18 23:59:57          0        0      0        0  \n",
       "2017-02-18 23:59:57          1        0      0        0  \n",
       "2017-02-18 23:59:57          0        0      0        0  \n",
       "2017-02-18 23:59:57          1        0      0        0  \n",
       "2017-02-18 23:59:57          1        0      0        0  \n",
       "2017-02-18 23:59:58          1        0      0        0  \n",
       "2017-02-18 23:59:58          1        0      0        0  \n",
       "2017-02-18 23:59:58          0        0      0        0  \n",
       "2017-02-18 23:59:58          1        0      0        0  \n",
       "2017-02-18 23:59:58          1        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "2017-02-18 23:59:59          1        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "2017-02-18 23:59:59          0        0      0        0  \n",
       "\n",
       "[3272043 rows x 10 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view = {}\n",
    "for i in rawd.action_type:\n",
    "    if i not in view:\n",
    "        view[i] = 0\n",
    "    view[i] += 1\n",
    "print(view)\n",
    "\n",
    "\n",
    "rawd['view'] = [1 if x == 'view' else 0 for x in rawd.action_type]\n",
    "rawd['deep_view'] = [1 if x == 'deep_view' else 0 for x in rawd.action_type]\n",
    "rawd['collect'] = [1 if x == 'collect' else 0 for x in rawd.action_type]\n",
    "rawd['share'] = [1 if x == 'share' else 0 for x in rawd.action_type]\n",
    "rawd['comment'] = [1 if x == 'comment' else 0 for x in rawd.action_type]\n",
    "\n",
    "rawd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "action_type\n",
      "share            459\n",
      "comment         4320\n",
      "collect        10884\n",
      "deep_view    1440744\n",
      "view         1815636\n",
      "Name: action_type, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f93c722beb8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H7nAC0qDbS+n5euJjXDywdI7d4QQMSSoiqB08XonbY/DM1jwiXfJy9pVZidE8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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93c722bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(rawd.action_type.groupby(rawd.action_type).count().sort_values())\n",
    "rawd.action_type.groupby(rawd.action_type).count().plot()\n",
    "\n",
    "# sns.distplot(rawd.action_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f93a58cde48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PY8zVQAj4NPA68D1jzKeA7cCDIhI3xnweeBw7nfTLItJvjPkhcJkx5lkgAtzkHHcl8G0n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\nJLUMBUlSy1CQJLUMBUlS6wdXnACIF0bwWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93a58fbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rawd.cate_id.groupby(rawd.cate_id).count().sort_values(ascending=False)\n",
    "rawd.cate_id.groupby(rawd.cate_id).count().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>action_type</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>view</th>\n",
       "      <th>deep_view</th>\n",
       "      <th>collect</th>\n",
       "      <th>share</th>\n",
       "      <th>comment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>11482147</td>\n",
       "      <td>492681</td>\n",
       "      <td>1_11</td>\n",
       "      <td>view</td>\n",
       "      <td>1487174400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>12070750</td>\n",
       "      <td>457406</td>\n",
       "      <td>1_14</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>12431632</td>\n",
       "      <td>527476</td>\n",
       "      <td>1_1</td>\n",
       "      <td>view</td>\n",
       "      <td>1487174400</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>13397746</td>\n",
       "      <td>531771</td>\n",
       "      <td>1_6</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-16</th>\n",
       "      <td>13794253</td>\n",
       "      <td>510089</td>\n",
       "      <td>1_27</td>\n",
       "      <td>deep_view</td>\n",
       "      <td>1487174400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id item_id cate_id action_type   timestamp  view  \\\n",
       "action_time                                                           \n",
       "2017-02-16   11482147  492681    1_11        view  1487174400     1   \n",
       "2017-02-16   12070750  457406    1_14   deep_view  1487174400     0   \n",
       "2017-02-16   12431632  527476     1_1        view  1487174400     1   \n",
       "2017-02-16   13397746  531771     1_6   deep_view  1487174400     0   \n",
       "2017-02-16   13794253  510089    1_27   deep_view  1487174400     0   \n",
       "\n",
       "             deep_view  collect  share  comment  \n",
       "action_time                                      \n",
       "2017-02-16           0        0      0        0  \n",
       "2017-02-16           1        0      0        0  \n",
       "2017-02-16           0        0      0        0  \n",
       "2017-02-16           1        0      0        0  \n",
       "2017-02-16           1        0      0        0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 保存结果\n",
    "rawd.to_pickle('../pkl/process.train.pkl')\n",
    "rawd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>cate_id</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>493659</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1486744638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>481181</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1486598042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>486720</td>\n",
       "      <td>1_11</td>\n",
       "      <td>1486684315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>559008</td>\n",
       "      <td>1_11</td>\n",
       "      <td>1487372097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>523054</td>\n",
       "      <td>1_23</td>\n",
       "      <td>1486972971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>523057</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1486972979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>523056</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486972977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>523051</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1486972961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>523053</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486972963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>494920</td>\n",
       "      <td>1_18</td>\n",
       "      <td>1486771984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>494922</td>\n",
       "      <td>1_18</td>\n",
       "      <td>1486771991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>523059</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486972985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>494925</td>\n",
       "      <td>1_18</td>\n",
       "      <td>1486771998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>494926</td>\n",
       "      <td>1_18</td>\n",
       "      <td>1486772003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>494927</td>\n",
       "      <td>1_12</td>\n",
       "      <td>1486772010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>153825</td>\n",
       "      <td>3_2</td>\n",
       "      <td>1472447553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>515065</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1486906351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>515064</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1486906339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>515066</td>\n",
       "      <td>1_11</td>\n",
       "      <td>1486906352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>515061</td>\n",
       "      <td>1_9</td>\n",
       "      <td>1486906304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>515060</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1486906301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>515063</td>\n",
       "      <td>1_9</td>\n",
       "      <td>1486906328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>515069</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1486906363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>515068</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1486906360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>286234</td>\n",
       "      <td>1_5</td>\n",
       "      <td>1479903361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>195518</td>\n",
       "      <td>1_5</td>\n",
       "      <td>1474884541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>286239</td>\n",
       "      <td>1_5</td>\n",
       "      <td>1479804555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>323058</td>\n",
       "      <td>1_4</td>\n",
       "      <td>1481355556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>545968</td>\n",
       "      <td>1_7</td>\n",
       "      <td>1487221757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>545967</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1487221752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75183</th>\n",
       "      <td>465427</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486351190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75184</th>\n",
       "      <td>465426</td>\n",
       "      <td>1_14</td>\n",
       "      <td>1486351180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75185</th>\n",
       "      <td>465429</td>\n",
       "      <td>3_1</td>\n",
       "      <td>1486351200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75186</th>\n",
       "      <td>437550</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1485831707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75187</th>\n",
       "      <td>437555</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1485831774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75188</th>\n",
       "      <td>494841</td>\n",
       "      <td>1_17</td>\n",
       "      <td>1486771642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75189</th>\n",
       "      <td>436400</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1485822156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75190</th>\n",
       "      <td>323640</td>\n",
       "      <td>1_12</td>\n",
       "      <td>1481364108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75191</th>\n",
       "      <td>427478</td>\n",
       "      <td>1_2</td>\n",
       "      <td>1485673262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75192</th>\n",
       "      <td>393392</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1484549762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75193</th>\n",
       "      <td>522325</td>\n",
       "      <td>3_2</td>\n",
       "      <td>1486968647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75194</th>\n",
       "      <td>522327</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486968653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75195</th>\n",
       "      <td>522320</td>\n",
       "      <td>1_14</td>\n",
       "      <td>1486968610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75196</th>\n",
       "      <td>522321</td>\n",
       "      <td>1_3</td>\n",
       "      <td>1486968619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75197</th>\n",
       "      <td>522322</td>\n",
       "      <td>1_12</td>\n",
       "      <td>1486968631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75198</th>\n",
       "      <td>486115</td>\n",
       "      <td>1_3</td>\n",
       "      <td>1486650128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75199</th>\n",
       "      <td>486118</td>\n",
       "      <td>1_19</td>\n",
       "      <td>1486650157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75200</th>\n",
       "      <td>486119</td>\n",
       "      <td>1_8</td>\n",
       "      <td>1486650157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75201</th>\n",
       "      <td>522328</td>\n",
       "      <td>3_6</td>\n",
       "      <td>1486968655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75202</th>\n",
       "      <td>522329</td>\n",
       "      <td>1_11</td>\n",
       "      <td>1486968657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75203</th>\n",
       "      <td>513838</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1486899453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75204</th>\n",
       "      <td>513839</td>\n",
       "      <td>1_3</td>\n",
       "      <td>1486899454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75205</th>\n",
       "      <td>513830</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486899427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75206</th>\n",
       "      <td>513831</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1486899428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75207</th>\n",
       "      <td>513832</td>\n",
       "      <td>1_6</td>\n",
       "      <td>1486899431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75208</th>\n",
       "      <td>513833</td>\n",
       "      <td>1_12</td>\n",
       "      <td>1486899433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75209</th>\n",
       "      <td>513834</td>\n",
       "      <td>1_11</td>\n",
       "      <td>1486899435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75210</th>\n",
       "      <td>513836</td>\n",
       "      <td>1_12</td>\n",
       "      <td>1486899443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75211</th>\n",
       "      <td>513837</td>\n",
       "      <td>1_1</td>\n",
       "      <td>1486899449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75212</th>\n",
       "      <td>398019</td>\n",
       "      <td>1_14</td>\n",
       "      <td>1484638126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75213 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       item_id cate_id   timestamp\n",
       "0       493659     1_1  1486744638\n",
       "1       481181    1_17  1486598042\n",
       "2       486720    1_11  1486684315\n",
       "3       559008    1_11  1487372097\n",
       "4       523054    1_23  1486972971\n",
       "5       523057     1_7  1486972979\n",
       "6       523056     1_6  1486972977\n",
       "7       523051    1_17  1486972961\n",
       "8       523053     1_6  1486972963\n",
       "9       494920    1_18  1486771984\n",
       "10      494922    1_18  1486771991\n",
       "11      523059     1_6  1486972985\n",
       "12      494925    1_18  1486771998\n",
       "13      494926    1_18  1486772003\n",
       "14      494927    1_12  1486772010\n",
       "15      153825     3_2  1472447553\n",
       "16      515065     1_7  1486906351\n",
       "17      515064     1_7  1486906339\n",
       "18      515066    1_11  1486906352\n",
       "19      515061     1_9  1486906304\n",
       "20      515060     1_7  1486906301\n",
       "21      515063     1_9  1486906328\n",
       "22      515069    1_17  1486906363\n",
       "23      515068     1_7  1486906360\n",
       "24      286234     1_5  1479903361\n",
       "25      195518     1_5  1474884541\n",
       "26      286239     1_5  1479804555\n",
       "27      323058     1_4  1481355556\n",
       "28      545968     1_7  1487221757\n",
       "29      545967     1_1  1487221752\n",
       "...        ...     ...         ...\n",
       "75183   465427     1_6  1486351190\n",
       "75184   465426    1_14  1486351180\n",
       "75185   465429     3_1  1486351200\n",
       "75186   437550    1_17  1485831707\n",
       "75187   437555    1_17  1485831774\n",
       "75188   494841    1_17  1486771642\n",
       "75189   436400     1_1  1485822156\n",
       "75190   323640    1_12  1481364108\n",
       "75191   427478     1_2  1485673262\n",
       "75192   393392     1_1  1484549762\n",
       "75193   522325     3_2  1486968647\n",
       "75194   522327     1_6  1486968653\n",
       "75195   522320    1_14  1486968610\n",
       "75196   522321     1_3  1486968619\n",
       "75197   522322    1_12  1486968631\n",
       "75198   486115     1_3  1486650128\n",
       "75199   486118    1_19  1486650157\n",
       "75200   486119     1_8  1486650157\n",
       "75201   522328     3_6  1486968655\n",
       "75202   522329    1_11  1486968657\n",
       "75203   513838     1_1  1486899453\n",
       "75204   513839     1_3  1486899454\n",
       "75205   513830     1_6  1486899427\n",
       "75206   513831     1_1  1486899428\n",
       "75207   513832     1_6  1486899431\n",
       "75208   513833    1_12  1486899433\n",
       "75209   513834    1_11  1486899435\n",
       "75210   513836    1_12  1486899443\n",
       "75211   513837     1_1  1486899449\n",
       "75212   398019    1_14  1484638126\n",
       "\n",
       "[75213 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BYBZ4HPhQ4fEPAo8CB4EblVKtSqkgcBtwoLD2w4W1HwCeqnMfgnDFKBaBVUgBtZHB8EKj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\n/yBNgnGNt6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f93a8c3f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "old_news = pd.read_csv('../raw/all_news_info.csv')\n",
    "news = pd.read_csv('../raw/news_info.csv')\n",
    "\n",
    "# 查看分类类别\n",
    "old_news.cate_id.groupby(old_news.cate_id).count().plot()\n",
    "news.cate_id.groupby(news.cate_id).count().plot()\n",
    "\n",
    "old_news"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d_onews = pd.to_datetime((old_news.timestamp + 60*60*8) * 10 ** 9)\n",
    "old_news['date'] = d_onews\n",
    "# old_news.to_pickle('../pkl/process.all_news.pkl')\n",
    "\n",
    "d_news = pd.to_datetime((news.timestamp + 60*60*8) * 10 ** 9)\n",
    "news['date'] = d_news\n",
    "# news.to_pickle('../pkl/process.news.pkl')\n",
    "\n",
    "news.index = news.date\n",
    "old = news.sort_index(ascending=False)\n",
    "# old[old.cate_id == '1_1']\n",
    "old.to_pickle('../pkl/process.news.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
